IMAGE V1I1 June 2021

The PHQ-9 and GAD-7 are standardized measures used to monitor clinical outcomes as part of
Efficacy’s Clinical Governance strategy. These are increasingly used in robust mental health

research to indicate a diagnosis, a classification of severity and outcome monitoring within national
CBT therapy services. The GAD-7 is a measurement for Anxiety Disorders and the PHQ-9 is a
measurement for depression. The PHQ-9 and GAD-7 are designed to facilitate the recognition for
depressive disorders and anxiety disorders respectively. These are the national standard measures
routinely used by GP’s, therapists and psychiatrists as screening tools.
All analyses were performed using SPSS 11.0 (SPSS Inc, Illinois). Descriptive of the IES, GAD and
PHQ scores were presented using mean (standard deviation) range and median. A factor analysis
was performed to cluster the coping strategies and life’s priorities during the Covid-19 situation.
Finally, logistic regression analysis was performed to determine predictors (the reduced factors for
the coping strategies and changes in priorities determined from the factor analysis) indicative of
severe psychiatric symptoms or post-traumatic stress disorder. Statistical significance was set at
P<0.05.

4.0 Results

The survey received a total of 642 participants, with an average of (32.82±6.41) years old, 136 male
participants(21.18%), 506 female participants(78.81%), among them 205(31.93) with working
experience less than 5 years, 325(50.62%) more than 5 years, and 112(17.45) more than 10 years
working experience. As for their education level, 125(19.47%) master and above, 284(44.24%)
undergraduates, 233(36.29%) diploma and below.
The test result shows 29.44% have encountered anxiety while 36.45% have depression. Working
experience, education levels, the stress level and current personal health would affect the
participant's mental health, there is statistical significance in the difference (P>0.05).

Table 1: Participants’ Demographic Data and Mental Conditions

Sample Anxiety (GAD-7 >4) Depression (PHQ-9>4)
N=642
Count x2 P Count x2 P

Gender 0.414 0.520 51(37.5) 0.082 0.774
Male 136(21.18) 37(27.21)

Female 506(78.81) 162(30.03) 183(36.17)

Age 43(6.70) 15(34.88) 0.996 0.608 2.109
18-25 0.348

26-45 563(87.69) 162(28.77) 20(45.51)
46-65 36(5.61) 12(33.33) 202(35.87)
12(33.33)

Working Experience 21.044 18.021 0.000
Junior<5 years 0.000
Senior>5 years 205(31.93) 40(19.51) 59(28.78)
Expert>10 years 325(50.62) 100(30.77) 116(35.69)
112(17.45) 49(43.75) 59(52.58)
Education Level
Master and above 125(19.47) 9.000 0.011 9.306
Undergraduate 284(44.24) 26(20.80) 0.000
Diploma and below 233(36.29) 80(28.17) 31(24.80)
83(25.62) 109(38.38)
94(40.34)
Stress Level (Reflect 86.087 0.000
of Stressors in 56.551
Scores)
Not worry(<20) 56(8.72) 0.000 7(12.50)
Slightly worry(21- 342(53.27) 96(29.07)
58(9.03) 4(7.14) 29(50.00)
69(20.18)

50

35) 23(39.66)

Moderately worry 121(18.85) 64(52.89)
(36-45) 48(39.67)

Really worry 65(10.12) 45(69.23) 38(58.46)
(46-55)
Extremely worry

(>56)

Health Status 41.233 43.601 0.000
Good 545(84.89) 0.000 171(31.38)

Normal 89(13.86) 134(24.59) 56(62.92)
Deteriorate 6(0.93) 50(56.18) 6(100.00)
Bad 2(0.31) 4(66.67) 1(50.00)

1(50.00)

Note: the comparison of Stress Level and Demographic Data, 1. Anxiety measures P<0.05, 2.
Depression measures P<0.05

The anxiety was mostly due to the stressors caused by Covid-19 and their current health status,
while their working experience is the buffer (P<0.05). The education level has no connection with
the anxiety measures (Table 2). Stressors due to Covid-19 and health status(P<0.05) is the main
cause of depression, with the working experience as the buffer (P<0.05). The educational level has
no significant connection with the depression measures (Table3).

Table 2: Logistic Regression for Binary Outcomes (Anxiety)

Method B SE Wald OR P 95%CI

Working Experience 12.63 0.002
(1) -1.01 0.28 12.62 0.37 0.005 0.21~0.64

(2) -0.54 0.25 4.64 0.59 0.031 0.36~0.95
Stress Level 0.62 0.08 59.26 1.86 0.000 1.59~4.36

Health Status 1.04 0.22 22.67 2.84 0.000 1.85~4.36

Note: Using Working Experience, Expert as reference, creating 2 dummy variables, (1): Junior =1, Non
Junior = 0, (2): Senior = 1, Non Senior = 0

Table 3: Logistic Regression for Binary Outcomes (Depression)

Method B SE Wald OR P 95%CI

Working Experience 11.93 0.003

(1) -0.86 0.26 10.92 0.37 0.001 0.25~0.70
(2) -0.70 0.24 8.66 0.42 0.003 0.31~0.79

Stress Level 0.47 0.08 38.62 0.50 0.000 1.38~1.85
Health Status 1.15 0.23 26.00 3.16 0.000 2.03~4.91

Note: Using Working Experience, Expert as reference, creating 2 dummy variables, (1): Junior =1, Non
Junior = 0, (2): Senior = 1, Non Senior = 0

The hypothesis in this study focus on the relationship between stressors (independent variable)
and employees’ mental condition. In the model, employees’ mental health is the dependent variable
and their working experience is the mediator. All these variables were measured by the working
adults’ responses. Each structural path of the model represents a possible relationship between the
two variables and can be analyzed for significance. The path coefficient may be considered
equivalent to a regression coefficient (β) and measures the unidirectional relationship between two
constructs.
As shown by the tables, under Covid-19 the public has been tested with anxiety 29.44% and
depression 36.45%.

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The finding indicates a significant impact of the stressors to the employees. In addition to the
biological health and safety of the general population as well as health care professionals, a stream
of research has also addressed potential threats to the mental/psychological health and domestic
safety challenges posed by the COVID-19 crisis. It is clear that psychological well-being and physical
safety are intrinsically interconnected and cannot be reasonably categorized as fully separate safety
dimensions. Severe depression and anxiety can lead to self-harm and even suicide and domestic
violence, which all affect the physical well-being of individuals. Nevertheless, the focus of studies in
this stream is on the indirect mental health impacts of this global epidemic rather than the
biological and clinical aspects.
Disease outbreaks not only disrupt basic life activities and impede economic growth; they can also
elicit both acute and long-term effects on individuals' well-being. In other words, the toll on
individuals is not just physical and financial, but emotional as well. Many studies have consistently
found relationships between the occurrence of infectious disease outbreaks and a host of
psychological and behavioural consequences. Among the negative psychological consequences that
have been most frequently reported are greater incidence of depression and psychological distress,
worry, functional impairment, anxiety about being infected, and reduced quality of life (and
subjective well-being.
In terms of behavioural consequences, exposure to outbreaks also resulted in preventive
behaviours such as improved hygienic practices, seeking medical assistance and engaging in social
distancing and isolation. The above psychological and behavioural consequences are experienced
by the broader workforce but perhaps more acutely by essential workers. In studies focusing on
health care workers, they often report concerns about the (non) availability of personal protective
equipment (PPE), personal safety, vaccine availability, caregiving responsibilities at home, and
prioritizing the well-being of family members.
Psychological distress also occurs as a result of mitigation strategies (e.g., social distancing, home
containment, and travel restrictions) aimed to prevent the spread of the disease. For example, in a
study of health care workers in a treatment facility during the SARS outbreak, Maunder et al. (2003)
reported incidents of professional isolation arising from the use of protective masks and
observance of non-physical contact with coworkers reduced morale among health care workers,
and refusal to work among administrative and professional staff. Bai et al. (2004) investigated
reactions of health care workers and professional staff shortly after 57 health care workers were
quarantined due to the SARS epidemic. Results revealed that 20% of the participants reported
feeling stigmatized, ostracized and rejected in their neighbourhoods due to their hospital work,
while 9% expressed reluctance to return to work or had thoughts of quitting their job. Beyond
those in the health care sector, in response to disease outbreaks, individuals in many organizations
and industries have to endure harsh workplace conditions such as limited availability of social and
work support, increased work demands, irregular work hours, inadequate work benefits, and poor
access to healthcare.
These challenging work conditions often increase general health complaints such as fatigue, upset
stomach, sleeping difficulties and headaches. Additionally, school closures and suspension of
religious activities arising from social distancing measures further exacerbated these adverse
psychological difficulties and contributed to serious financial strain. Taken as a whole, these studies
suggest that disease outbreaks can have pervasive consequences for mental health and well-being
across the workforce.
The finding of the study indicates the partial moderating effect of working experiences towards the
relationship between stressors and mental health. This is possibly due to the higher working
experience the more stress the employees are bearing, thus bringing pressure to perform in the
workplace. Employees who start to feel the "pressure to perform" can get caught in a downward
spiral of increasing effort to meet rising expectations with no increase in job satisfaction. The
relentless requirement to work at optimum performance takes its toll in job dissatisfaction,
employee turnover, reduced efficiency, illness and even death.

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5.0 Conclusion

Having a perceived sense of control reduced perceived risk and available social support were
important in the health and wellbeing of the staff during the Covid-19 crisis. Hence, clear directives
and disease information, as well as being able to ventilate and voice their concerns, are important
in empowering staff and in turn, improving their ability to cope. Making all protective measures to
all employees did not just protect them physically – it made them feel safer. The sense of control
and the perceived risk level appears to be the actual determinants of emotional impact, despite the
actual risk level.
Employees in a safe and supportive environment feel better and are healthier, which in turn leads
to reduced absenteeism, enhanced motivation, improved productivity and a positive organization's
image. The prevention of occupational accidents and diseases, the promotion of healthy working
life and the building of a preventive culture is a shared responsibility of governments, employers
and workers, health professionals and societies as a whole.

References

Bai Y, Lin CC, Lin CY, et al.: Survey of stress reactions among health care workers involved with the
SARS outbreak. Psychiatr Serv. 2004; 55(9): 1055–1057.

Bao Y, Sun Y, Meng S, et al.: 2019-nCoV epidemic: address mental health care to empower society.
Lancet. 2020; 395(10224): e37–e38.

Beck AT, Alford BA: Depression: Causes and treatment. University of Pennsylvania Press. 2009; 405.
disorders. Work. 2014; 48(4): 579–589.

Ferguson NM, Cummings DA, Fraser C, et al.: Strategies for mitigating an influenza pandemic.
Nature. 2006; 442(7101): 448–52.

Gajendran RS, Harrison DA: The good, the bad, and the unknown about telecommuting: meta-
analysis of psychological mediators and individual consequences. J Appl Psychol. 2007; 92(6):
1524.

Gao J, Zheng P, Jia Y, et al.: Mental Health Problems and Social Media Exposure During COVID-19
Outbreak. Available at SSRN 3541120. 2020.

Huang Y, Zhao N: Generalized anxiety disorder, depressive symptoms and sleep quality during
COVID-19 epidemic in China: a web-based cross-sectional survey. medRxiv. 2020.

Karasek RA: Job demands, job decision latitude, and mental strain: Implications for job redesign.
Adm Sci Q. 1979; 24(2): 285–308.

Karasek R, Theorell T: Healthy work: stress, productivity, and the reconstruction of working life.
New York, Basic Books. 1990; 66(4): 525–526.

Koh D: Occupational risks for COVID-19 infection. Occup Med (Lond). 2020; 70(1): 3–5.
Lai J, Ma S, Wang Y, et al.: Factors Associated With Mental Health Outcomes Among Health Care

Workers Exposed to Coronavirus Disease 2019. JAMA Netw Open. 2020; 3(3): e203976–
e203976.
Xiao H, Zhang Y, Kong D, et al.: The Effects of Social Support on Sleep Quality of Medical Staff
Treating Patients with Coronavirus Disease 2019 (COVID-19) in January and February 2020 in
China. Med Sci Monit. 2020; 26: e923549.
Yang Y, Li W, Zhang Q, et al.: Mental health services for older adults in China during the COVID-19
outbreak. Lancet Psychiatry. 2020; 7(4): e19.
Zhang J, Wu W, Zhao X, et al.: Recommended psychological crisis intervention response to the 2019
novel coronavirus pneumonia outbreak in China: a model of West China Hospital. Precision
Clinical Medicine. 2020a; 3(1): 3–8.

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